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#           This file was automatically generated from src/transformers/models/openai_privacy_filter/modular_openai_privacy_filter.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
#             the file from the modular. If any change should be done, please apply the change to the
#                          modular_openai_privacy_filter.py file directly. One of our CI enforces this.
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# Copyright 2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...utils import auto_docstring


OPENAI_PRIVACY_FILTER_SPAN_LABELS = (
    "O",
    "account_number",
    "private_address",
    "private_date",
    "private_email",
    "private_person",
    "private_phone",
    "private_url",
    "secret",
)

OPENAI_PRIVACY_FILTER_NER_LABELS = ("O",) + tuple(
    f"{prefix}-{label}"
    for label in OPENAI_PRIVACY_FILTER_SPAN_LABELS
    if label != "O"
    for prefix in ("B", "I", "E", "S")
)


@auto_docstring(checkpoint="openai/privacy-filter")
@strict
class OpenAIPrivacyFilterConfig(PreTrainedConfig):
    model_type = "openai_privacy_filter"
    attribute_map = {
        "num_experts": "num_local_experts",
    }
    default_theta = 150000.0
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }
    base_model_ep_plan = {
        "layers.*.mlp.router": "ep_router",
        "layers.*.mlp.experts.gate_up_proj": "grouped_gemm",
        "layers.*.mlp.experts.gate_up_proj_bias": "grouped_gemm",
        "layers.*.mlp.experts.down_proj": "grouped_gemm",
        "layers.*.mlp.experts.down_proj_bias": "grouped_gemm",
        "layers.*.mlp.experts": "moe_tp_experts",
    }
    num_hidden_layers: int = 8
    num_local_experts: int = 128
    vocab_size: int = 200064
    hidden_size: int = 640
    intermediate_size: int = 640
    head_dim: int = 64
    num_attention_heads: int = 14
    num_key_value_heads: int = 2
    sliding_window: int = 128
    tie_word_embeddings: bool = False
    initializer_range: float = 0.02
    max_position_embeddings: int = 131072
    rms_norm_eps: float = 1e-5
    rope_parameters: dict | None = None
    attention_dropout: float | int = 0.0
    num_experts_per_tok: int = 4
    router_aux_loss_coef: float = 0.001
    output_router_logits: bool = False
    use_cache: bool = True
    pad_token_id: int | None = 199999
    bos_token_id: int | None = None
    eos_token_id: int | list[int] | None = 199999
    attention_bias: bool = True
    classifier_dropout: float = 0.0

    def __post_init__(self, **kwargs):
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads
        self.head_dim = self.head_dim if self.head_dim is not None else self.hidden_size // self.num_attention_heads

        if self.rope_parameters is None:
            self.rope_parameters = {
                "rope_type": "yarn",
                "factor": 32.0,
                "beta_fast": 32.0,
                "beta_slow": 1.0,
                "truncate": False,
                "original_max_position_embeddings": 4096,
            }

        requested_num_labels = kwargs.pop("num_labels", len(OPENAI_PRIVACY_FILTER_NER_LABELS))
        if self.id2label is None and requested_num_labels == len(OPENAI_PRIVACY_FILTER_NER_LABELS):
            self.id2label = dict(enumerate(OPENAI_PRIVACY_FILTER_NER_LABELS))
        elif self.id2label is None:
            self.num_labels = requested_num_labels
        if self.label2id is None and self.id2label is not None:
            self.label2id = {label: idx for idx, label in self.id2label.items()}

        super().__post_init__(**kwargs)


__all__ = ["OpenAIPrivacyFilterConfig"]
